1. Technical Field
The embodiments herein generally relate to classifying and annotating multimedia content, and more particularly to classifying and annotating images based on a user context and intention.
2. Description of the Related Art
Rapid technological development in handheld devices such as digital cameras, smartphones, and any other camera enabled devices enables capturing and processing images ubiquitously. The number of such images being captured and stored is rapidly increasing. Images that are captured can be shared with private connections, over a social network, or with the public. With huge volumes of such images, classification of the images has become essential for organizing, retrieving, and sharing these images. When classification of images is performed manually, users have to spend a substantial amount of time and effort. Images can be searched and retrieved by annotating them with tags. Tagging images with inaccurate information results in large collections of unrelated images being lumped together, making it difficult to organize.
Existing approaches that attempt to address the problem of image classification typically capture the location of a camera and tag the image based on the camera location. However, the location of the camera itself may not be directly relevant to the image or to the intent or purpose of the photographer in capturing the image. Annotating the image with the location data of the camera may thus often result in inaccurate image classification. Further, the same image can have different meanings to different users or for the same user at different occasions, depending upon the intended purpose. It may also have the same meaning in general (e.g., social photos), but different users may have different relationships with objects of an image. For example, a photo of a user in a business context may have a very different meaning and may hence require different annotations than a photo from the same user in a social context for effective searching, retrieving and sharing in a targeted manner.
Existing approaches that are simply based on available data generally do not suggest how to individualize annotations or how to prioritize classifications when there are many different context-dependent possibilities or classifications, as they do not determine which of the available data is relevant to the purpose of the user. The existing approaches also generally do not improve the fit between the image and the annotation with time either. Accordingly, there remains a need for a system and method for annotating and classifying images based on a user context, and for improving accuracy in annotating the images over time.